I would love to run a Deep Q-Learning algorithm or some type of Multi Agent Reinforcement Learning algorithm. I tried a deep q-learning approach locally, but it looks like my laptop will take months before I can even see any sort of results .

I'm trying to apply policy gradient approach from various reinforcement learning papers. My naive 3 layer neural network is currently on my laptop and can only run once a day (training over-night). Would greatly appreciate a voucher to speeding things up on GCP using their GPUs

Just found Halite, but figured I would respond while these are still available. I'd like to look at TD/q-learning and policy gradient on top of hand-engineered actions/strategies. I'd also like to try using imitation learning for initial policies.

The voucher would be really helpful for the deep RL and inverse RL strategies I'm trying to implement based on various publications I've been looking at. It seems like any agents I try to train locally will not produce any results within a reasonable amount of time. Thanks!

I want to create better navigation to make the ships move better in fleet formations and tactical combat. I see many bots moving out in little waves and feel this should be more like a dog-fight than it currently is.

A large part of the strategy is navigation and predicting opponents moves which will be computationally expensive. Google credits will help me with that.

Edit: An update to my strategy: I want to take in a wider amount of models and be able to train them. Training rounds are being bogged down by memory restrictions on my home machine. We can worry about dogfights later...

I want to use a reinforcement learning algorithm, much like the Atari Deep Q learning algorithm, but using game specific knowledge to make high level decisions. To do this, I can start by looking at replays. But the important aspect will be training by playing my bot against older versions of itself.

Running this in the cloud will allow me to really parallelize this game playing, making the training time more reasonable.

I would like to use the voucher to better implement my machine learning algorithms to enhance my current bot and keep them running on the GPU. It's also a great learning experience for me (relatively new in ML/DL).

If they are still available, I would like to have a voucher to do some long term ML training. My dataset is getting quite huge, and my laptop's CPU is good enough for the actual playing, but for training, it's not cutting it anymore, and I would like to train it on some faster CPU (or GPU if available).

If anyone is interested, I'm using a variation on DQN and the Starcraft II shared knowledge/multi agent scaling whitepaper. Essentially, there is a ConvNet that represents some kind of general strategy/decision maker, and a NN that takes the ConvNet's output, and a ship's current state, and output a specific action for this ship. The goal is for the ConvNet and the NN to learn to talk to each other, so that effectively, there is a general higher-level strategy (the ConvNet), and a lower level/micromanagement with the Neural Network. There will be some shared experience with all of the ship's results/output feeding back into the ConvNet's thus hopeuflly accelerating the learning of the ConvNet talking to the NN, but so far, it has not been very successful. It has learned to (mostly) avoid other ship, but it has not learned about docking yet, and spends most of its time doing nothing.

My bottlenecks right now is time (Fulltime job + kids + laptop's CPU), and with the voucher, I feel that it would fix the CPU part of it : Faster CPU means generating data faster, and faster (hopefully way faster) training.

I have been doing a lot of reading about RL agents that use deep learning, but I haven't yet had the time/application/resources in combination to work on actually building a bot using anything beyond simple Q learning with hand-coded features. I hope to change that this holiday break with Halite and (hopefully) credits on GCE.

I would like to use RL and evolutionary strategies in selecting targets/planets and in choosing the appropriate amount of ships to use in capturing a planet. Having a large number of processors to run the various options would be very helpful.